This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution.
SPSS: Apply & Interpret Logistic Regression Models
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3 hours to complete
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Recently updated!
September 2025
Assessments
6 assignments
Taught in English
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There are 2 modules in this course
This module introduces learners to the foundational principles of logistic regression and equips them with hands-on skills in SPSS for managing variables, configuring the data environment, and interpreting statistical outputs. Learners will explore both theoretical concepts and practical applications, including variable setup, SPSS navigation, model output interpretation, and foundational logistic modeling techniques. By the end of this module, learners will be capable of preparing, analyzing, and interpreting logistic regression models using SPSS and supporting tools like MS Excel.
What's included
7 videos3 assignments
This module focuses on applying logistic regression techniques through real-world case studies and interpreting model results in SPSS. Learners will work with datasets such as heart pulse and smoking behavior, construct logistic equations, analyze variable significance, and interpret model output to draw actionable conclusions. The module emphasizes evaluating model performance and improving prediction accuracy using statistical evidence from SPSS output tables.
What's included
7 videos3 assignments
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